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orsum: a Python package for filtering and comparing enrichment analyses using a simple principle
BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the compar...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308244/ https://www.ncbi.nlm.nih.gov/pubmed/35870894 http://dx.doi.org/10.1186/s12859-022-04828-2 |
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author | Ozisik, Ozan Térézol, Morgane Baudot, Anaïs |
author_facet | Ozisik, Ozan Térézol, Morgane Baudot, Anaïs |
author_sort | Ozisik, Ozan |
collection | PubMed |
description | BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the comparison of results obtained from different enrichment analyses. An approach to overcome these issues is using down-sized annotation collections composed of non-redundant terms. However, down-sized collections are generic and the level of detail may not fit the user’s study. Other available approaches include clustering and filtering tools, which are based on similarity measures and thresholds that can be complicated to comprehend and set. RESULT: We propose orsum, a Python package to filter enrichment results. orsum can filter multiple enrichment results collectively and highlight common and specific annotation terms. Filtering in orsum is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes; the remaining more significant term becomes the representative term for the discarded term. This principle ensures that the main biological information is preserved in the filtered results while reducing redundancy. In addition, as the representative terms are selected from the original enrichment results, orsum outputs filtered terms tailored to the study. As a use case, we applied orsum to the enrichment analyses of four lists of genes, each associated with a neurodegenerative disease. CONCLUSION: orsum provides a comprehensible and effective way of filtering and comparing enrichment results. It is available at https://anaconda.org/bioconda/orsum. |
format | Online Article Text |
id | pubmed-9308244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93082442022-07-24 orsum: a Python package for filtering and comparing enrichment analyses using a simple principle Ozisik, Ozan Térézol, Morgane Baudot, Anaïs BMC Bioinformatics Software BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the comparison of results obtained from different enrichment analyses. An approach to overcome these issues is using down-sized annotation collections composed of non-redundant terms. However, down-sized collections are generic and the level of detail may not fit the user’s study. Other available approaches include clustering and filtering tools, which are based on similarity measures and thresholds that can be complicated to comprehend and set. RESULT: We propose orsum, a Python package to filter enrichment results. orsum can filter multiple enrichment results collectively and highlight common and specific annotation terms. Filtering in orsum is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes; the remaining more significant term becomes the representative term for the discarded term. This principle ensures that the main biological information is preserved in the filtered results while reducing redundancy. In addition, as the representative terms are selected from the original enrichment results, orsum outputs filtered terms tailored to the study. As a use case, we applied orsum to the enrichment analyses of four lists of genes, each associated with a neurodegenerative disease. CONCLUSION: orsum provides a comprehensible and effective way of filtering and comparing enrichment results. It is available at https://anaconda.org/bioconda/orsum. BioMed Central 2022-07-23 /pmc/articles/PMC9308244/ /pubmed/35870894 http://dx.doi.org/10.1186/s12859-022-04828-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Ozisik, Ozan Térézol, Morgane Baudot, Anaïs orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title | orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title_full | orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title_fullStr | orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title_full_unstemmed | orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title_short | orsum: a Python package for filtering and comparing enrichment analyses using a simple principle |
title_sort | orsum: a python package for filtering and comparing enrichment analyses using a simple principle |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308244/ https://www.ncbi.nlm.nih.gov/pubmed/35870894 http://dx.doi.org/10.1186/s12859-022-04828-2 |
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